{"id":"W3128167104","doi":"10.1016/j.jhydrol.2021.126056","title":"Analyzing streamflow variation in the data-sparse mountainous regions: An integrated CCA-RF-FA framework","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Streamflow; Environmental science; Lag; Meltwater; Snow; Precipitation; Surface runoff; Watershed; Hydrology (agriculture); Drainage basin; Meteorology; Geology; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002202918,0.0007187219,0.0008161748,0.001986688,0.0004960331,0.0008559916,0.0013148,0.000815863,0.0006890344],"category_scores_gemma":[0.004290758,0.0003943006,0.001701818,0.001951891,0.0005660977,0.001211238,0.000942563,0.0006567257,0.0001150528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005863735,"about_ca_system_score_gemma":0.001695012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03651823,"about_ca_topic_score_gemma":0.02651671,"domain_scores_codex":[0.999202,0.0002535638,0.00004703543,0.000266404,0.0001461236,0.00008491421],"domain_scores_gemma":[0.9986441,0.0006848472,0.0001953051,0.0001269048,0.0002766734,0.00007223387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003751333,0.00005755821,0.0155417,0.00007321562,0.0002342053,0.0001966717,0.00009983958,0.9144409,0.001470887,0.008197412,0.0005641282,0.05908599],"study_design_scores_gemma":[0.000002001103,0.00001222132,0.001876821,0.00000418747,0.00001835045,0.00002421765,0.00001095145,0.9952778,0.00009413846,0.002402118,0.000268574,0.000008630162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05722659,0.0004700882,0.9403864,0.0001782804,0.00001947484,0.00005674595,0.0003946026,0.0004485534,0.0008193091],"genre_scores_gemma":[0.7391302,0.0005399462,0.2576938,0.0001007018,0.0001196731,0.000176637,0.001305495,0.00008273176,0.0008507777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03651823,"threshold_uncertainty_score":0.07261133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02764506936370543,"score_gpt":0.2750564628116566,"score_spread":0.2474113934479512,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}